amer shebani (5 resultados)

Paperback. Condición: Brand New. 01 edition. 64 pages. 8.66x5.91x0.15 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The aims of this work are the identification and control of non-linear systems. Investigation of control of non-linear systems using PID controller presented. An alternative artificial neural network to overcome the limitations associated with the backpropagation algorithm such as slow convergence and construction complexity is the radial basis function neural network (RBFNN) which has been widely used for solving many complex problems. The RBFNN used on this work to control of nonlinear systems. 64 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shebani AmerDr. Amer Shebani has obtained his PhD degree in control engineering in 2017. He worked in power plant as instrumentation and control engineer, and then, he worked as a lecturer on higher education. He is the author of se.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The aims of this work are the identification and control of non-linear systems. Investigation of control of non-linear systems using PID controller presented. An alternative artificial neural network to overcome the limitations associated with the backpropagation algorithm such as slow convergence and construction complexity is the radial basis function neural network (RBFNN) which has been widely used for solving many complex problems. The RBFNN used on this work to control of nonlinear systems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The aims of this work are the identification and control of non-linear systems. Investigation of control of non-linear systems using PID controller presented. An alternative artificial neural network to overcome the limitations associated with the backpropagation algorithm such as slow convergence and construction complexity is the radial basis function neural network (RBFNN) which has been widely used for solving many complex problems. The RBFNN used on this work to control of nonlinear systems.